1. Field of the Invention
The present invention relates to the field of medical genetics.
2. Discussion of the Related Art
Currently there is much interest in the use of haplotype data in the genetics of common disease. Investigators are faced with the considerable challenge of how many and which variants to genotype in a given candidate gene for haplotype determination. Gabriel et al. sequenced 13 megabases across the genome in subjects from Africa, Europe, and Asia. They showed that the human genome is organized in haplotype blocks (most of which are longer than 10 kb), with three to five commonly occurring (>5%) haplotypes per block. Only six to eight variants were sufficient to define the most common haplotypes in each block. The challenge is how to select these variants efficiently and affordably. In the protocol described here, the first stage is to genotype a number of variants that span a genomic region of interest. This is performed in a subset of the study population to minimize costs. These data are then used to determine the haplotypes in that region. The most frequently occurring haplotypes are then identified, and only those SNPs that are necessary to define these haplotypes (typically six or fewer such haplotypes) are then genotyped on large scale, yielding the most common haplotypes in a population for association analysis. The availability of family data assists this approach by facilitating unambiguous determination of haplotypes.
The insulin resistance syndrome (also called the metabolic syndrome) is a clustering of factors associated with an increased risk of coronary artery disease (CAD). The syndrome affects over 20% of adults in the United States, with the highest age specific prevalence rates in Mexican-Americans. Insulin resistance, whether or not it is accompanied by other features of the metabolic syndrome, has been associated with an increased risk of cardiovascular events and death.
There is evidence in the Framingham offspring study that three factors or syndrome clusters, underlie the clustering of basic risk variables that form the insulin resistance syndrome: a diabetic predisposing syndrome characterized by impaired glucose tolerance, a cardiovascular metabolic syndrome, and a hypertension syndrome. Numerous lines of evidence from epidemiological studies support the idea that these factors occur many years prior to the onset of overt coronary artery disease.
The clustering of insulin resistance, hypertension, central obesity, and dyslipidemia in the metabolic syndrome is receiving much attention as a risk factor for cardiovascular disease. The central component of this syndrome, insulin resistance, has been found to increase cardiovascular risk. In the San Antonio Heart Study, insulin resistance, estimated by homeostatic model assessment (HOMA), was an independent predictor of incident cardiovascular events over 8 years of follow-up. In the Helsinki Policemen Study, 970 men free of diabetes or CAD at baseline were followed for 22 years; those with the highest levels of insulin resistance as estimated by insulin area under the curve during oral glucose tolerance testing had the highest rates of CAD events and death. High fasting insulin concentrations were an independent predictor of ischemic heart disease events among 2103 non-diabetic Canadian men. A genetic basis for the components of the insulin resistance syndrome has been demonstrated by familial aggregation. For this reason, investigators have asked the question as to whether genetic determinants of insulin resistance also influence the other components of the metabolic syndrome.
As an example, lipoprotein lipase (LPL) plays a major role in lipid metabolism. Located on capillary endothelium, LPL hydrolyzes triglycerides of chylomicrons and very low density lipoproteins, generating free fatty acids and monoacylglycerol. Complete deficiency of LPL results in the familial chylomicronemia syndrome. Because LPL activity affects the concentration of triglycerides, an important cardiovascular risk factor, LPL has been studied as a candidate gene for atherosclerosis. Several studies have identified linkage and association of the LPL gene with hypertension, indirect or surrogate measurements of insulin resistance, dyslipidemia, obesity, and atherosclerosis. LPL is an excellent candidate connecting insulin resistance to atherosclerosis because it controls the delivery of free fatty acids (FFA) to muscle, adipose tissue, and vascular wall macrophages, wherein lipid uptake influences peripheral insulin sensitivity, central obesity, and foam cell formation.
Wu et al. demonstrated linkage of the LPL locus to systolic blood pressure in non diabetic relatives of Taiwanese subjects with type 2 diabetes. The HindIII polymorphism in intron 8 of the LPL gene has been associated with measurements of insulin resistance in normoglycemic Caucasian and Hispanic subjects and Chinese subjects. The Ser447Stop polymorphism has been found to be associated with decreased atherosclerosis risk. Both the HindIII and Ser447Stop polymorphisms are in the 3′ end of the LPL gene, downstream of a recombination hotspot.
The LPL gene has emerged as a candidate gene for features of metabolic syndrome, including insulin resistance. LPL hydrolyzes triglycerides carried in chylomicrons and very low density lipoproteins, the rate-limiting step in delivery of free fatty acids (FFA) to muscle and adipose tissue. By controlling the delivery of FFA to muscle, LPL may affect insulin sensitivity by influencing levels of intramyocellular lipid, which correlate with muscle insulin resistance. Also, LPL may influence insulin resistance by affecting FFA delivery to visceral adipose tissue, which is increasingly viewed as an endocrine organ, capable of secreting mediators of insulin resistance. LPL action also regulates the plasma triglyceride concentration, an important atherosclerosis risk factor. LPL activity indirectly raises HDL-cholesterol levels because LPL-mediated hydrolysis of VLDL provides surface components that merge with HDL3 to form HDL2 particles. LPL-mediated delivery of FFA and lipoprotein remnants to vessel wall macrophages plays a role in foam cell formation, an early event in the development of atherosclerotic plaque. Thus, functional variation in LPL may impact both insulin resistance and atherosclerosis.
Most studies that have reported association of the LPL gene with insulin resistance used only surrogate measurements of insulin resistance, including fasting glucose, fasting insulin, and insulin area under the curve (AUC) during oral glucose tolerance testing (OGTT). One study evaluated the steady state plasma glucose during the insulin suppression test. In addition, all except one of these studies only examined association of the intronic restriction fragment length polymorphisms PvuII and HindIII. Thus, current evidence that variation in LPL plays a role in insulin sensitivity has been indirect. Assessment of glucose infusion rate (GINF) during the euglycemic hyperinsulinemic clamp study is widely regarded as the most direct physiologic measurement of insulin sensitivity. An analysis of indices of insulin sensitivity in the Insulin Resistance Atherosclerosis Study showed that direct physiologic measurements of insulin sensitivity have a higher heritability than measures based on fasting values (such as HOMA). Thus, use of physiologic indices rather than simple fasting indices should provide more power to discover genes that contribute to insulin sensitivity.
While various polymorphisms in the 3′ end of LPL, such as HindIII, have been associated with surrogate measures of insulin resistance and with atherosclerosis, published reports of positive linkage or association of variation in LPL with indices of insulin sensitivity have typically examined only one or two single nucleotide polymorphisms. However, a haplotype-based analysis recently demonstrated an association of LPL 3′ end haplotypes with coronary artery disease in Mexican-Americans.
Published studies reporting association of the LPL gene with insulin resistance used only single variants, usually HindIII or PvuII. In some cases, the results are in conflict; studies have reported the T allele of HindIII associated with insulin resistance, others report the G allele associated with insulin resistance, and others show no association of HindIII with insulin resistance. This demonstrates a limitation of the common approach of examining one or two polymorphisms per candidate gene in an association study.
With the sequencing of the human genome it has become apparent that variation in individuals is quite extensive. There is increasing evidence that this variation is best described by groups of associated polymorphisms referred to as haplotypes.
Recent studies suggest that the extensive variation in human beings is best described by groups of associated polymorphisms referred to as haplotypes. Haplotypes encompass chromosomal blocks that have remained unbroken by recombination during the population evolutionary history of the gene. Haplotypes are more likely to identify disease associations than single polymorphisms because they reflect global gene structure and encompass the majority of common variation in a gene. Identification of a haplotype associated with increased or decreased disease risk should facilitate identification of the actual functional variant that affects disease risk, because this variant should lie on chromosome regions identified by that haplotype.
Thus, haplotypes capture the majority of common variation in a gene; consequently, the use of haplotypes is more likely to identify disease-variation associations than is the use of a random single polymorphism. Identification of a haplotype associated with increased or decreased disease risk should facilitate identification of the actual functional variant that affects disease risk, because this variant should lie on chromosomes identified by that haplotype. Genotyping to determine haplotype structure and frequencies is required for this type of analysis. A major challenge is determination and selection of the polymorphisms that will be used to determine haplotypes in a given population.
Currently there is much interest in the use of haplotype data in the genetics of common diseases, such as coronary artery disease and insulin resistance. Investigators are faced with the considerable challenge of how many and which variants or markers to genotype in a given candidate gene for haplotype determination. Gabriel et al. sequenced 13 megabases across the genome in subjects from Africa, Europe, and Asia; it was shown that the human genome is organized in haplotype blocks (most of which are longer than 10 kilobases), with three to five commonly occurring (>5%) haplotypes per block. Only six to eight variants were sufficient to define the most common haplotypes in each block. There is a need for a way to select these variants, or markers, efficiently and affordably.
Accordingly, the present invention provides such a method of selecting useful haplotypes, as well particular haplotypes useful for predicting predisposition to insulin resistance in humans, including Hispanics. These and other benefits are described hereinbelow.